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IEEE International Symposium on Robotics and Manufacturing Automation (IEEE-ROMA)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479957651

In this paper, the comparison of multi-objective evolutionary algorithm (MOEA) and Single-objective evolutionary algorithm (SOEA) in designing and optimizing the morphology of a Six Articulated-Wheeled Robot (SAWR) is presented. Results show that both methods are able to produce optimized SAWR which have smaller size with the capability to perform climbing motion. However, one of the solutions from the Pareto-set of MOEA is outperforming the fittest solution from SOEA. The solution is able to achieve the same performance of the fittest solution from SOEA and yet it is smaller in size. Besides that, another advantage of using MOEA is that MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance which provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm single-objective evolutionary algorithm evolutionary robotics evolved hybrid mobile robot

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Balancing Operation Cost and Carbon Emissions in multi-Energy Microgrid Scheduling Using Joint Operator evolutionary algorithm 36

Balancing Operation Cost and Carbon Emissions in Multi-Energ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Bin Li, Qincheng Li, Jia Liu, Zhitao Su, Hongye Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

A multi-energy microgrid (MEMG), incorporating a combined heat and power (CHP) system and electric vehicles (EVs), is an important infrastructure to meet multiple energy demands and reduce carbon emissions. However, operation cost minimization and carbon emissions minimization in the MEMG conflict with each other. Therefore, this paper proposes a multi-objective optimal scheduling model for the MEMG, aimed at achieving a balance between operation cost and carbon emissions. Moreover, a novel multi-objective evolutionary algorithm (MOEA) called joint operator evolutionary algorithm (JOEA) is developed to solve this model. Experimental results indicate that JOEA has excellent performance on the proposed multi-objective optimal scheduling problem. In addition, JOEA is compared with several well-known MOEAs on this engineering problem and 20 benchmark problems. The comparison results show that JOEA outperforms other MOEAs and has competitive performance.

关键词： multi-energy microgrid Operation cost Carbon emissions multi-objective evolutionary algorithm

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A Novel Approach to multi-level evolutionary Design Optimization of a MEMS Device

A Novel Approach to Multi-level Evolutionary Design Optimiza...

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9th International Conference on Electron Spectroscopy

作者： Farnsworth, Michael Benkhelifa, Elhadj Tiwari, Ashutosh Zhu, Meiling Cranfield Univ Decis Engn Ctr Coll Rd Cranfield MK43 0AL Beds England Cranfield Univ Microsyst Nanotechnol Ctr Cranfield MK43 0AL Beds England

ISBN: (纸本)9783642153228

This paper introduces a novel approach to the evolutionary design optimisation of an MEMS bandpass filter, incorporating areas of multi-disciplinary, multi-level and multi-objective design optimisation in the process. In order to demonstrate this approach a comparison is made to previous attempts to design similar bandpass filters, providing comparable results at a significant reduction in functional evaluations. In this endeavour, a circuit equivalent of the MEMS bandpass filter is evolved extrinsically using the SPICE Simulator.

关键词： multi-Disciplinary Optimisation multi-objective evolutionary algorithm multi-Level Optimisation MEMS Micro-Electro-Mechanical Systems Extrinsic Evolution

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Applying an evolutionary algorithm for the Analysis of Mental Disorders in Macro-urban Areas: The Case of Barcelona

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SPATIAL ECONOMIC ANALYSIS 2015年第3期10卷 270-288页

作者： Salinas-Perez, Jose Alberto Rodero-Cosano, Maria Luisa Garcia-Alonso, Carlos Ramon Salvador-Carulla, Luis Univ Loyola Andalucia Dept Psychol Sociol & Social Work Cordoba Spain Univ Loyola Andalucia Dept Math & Engn Cordoba Spain Univ Sydney Ctr Disabil Res & Policy Sydney NSW 2006 Australia

Spatial analysis is widely used to study geographic patterns of diseases. To locate groups of close spatial units where the treated prevalence is significantly high or low, the latest contribution is a tool based on a multi-objective evolutionary algorithm, which has not yet been used in macro-urban areas: this study is the first attempt for this purpose. To do so, spatial distribution of the treated prevalence of mental disorders in basic health areas was analysed within the Barcelona metropolitan zone during 2009. The results highlight inequitable zones that need further attention, and geographically weighted regression shows that socio-economic factors influence treated prevalence although there may be additional factors involved.

关键词： multi-objective evolutionary algorithm treated prevalence spatial clusters macro-urban areas

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Robustness Analysis in evolutionary multi-objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Robustness Analysis in Evolutionary Multi-Objective Optimiza...

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9th European Conference on evolutionary Computation in Combinatorial Optimization

作者： Barrico, Carlos Antunes, Carlos Henggeler Pires, Dulce Fernao INESC Rua Antero Quental 199 P-3000033 Coimbra Portugal

ISBN: (纸本)9783642010088

In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated Solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionary algorithm, which intervenes in the Computation of the fitness value assigned to Solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power How, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.

关键词： robustness analysis multi-objective evolutionary algorithm reactive power compensation problem

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An Improved Ideal Point Setting in multiobjective evolutionary algorithm Based on Decomposition

An Improved Ideal Point Setting in Multiobjective Evolutiona...

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International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

作者： Fan, Zhun Li, Wenji Cai, Xinye Lin, Huibiao Hu, Kaiwen Yin, Haibin Shantou Univ Dept Elect Engn Shantou 515063 Guangdong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781467383127

In this paper, we propose an improved ideal point setting method in the framework of MOEA/D. MOEA/D decomposes a multi-objective optimisation problem into a number of scalar optimisation problems and optimise them simultaneously. The performance of MOEA/D highly relates to its decomposition method, and the proposed ideal point setting approach is used in the weighted Tchebycheff (TCH) and penalty-based boundary intersection (PBI) decomposition approach. It expands the region of search in the objective space by transforming the original ideal point into its symmetric point and changes the search direction of each subproblems in MOEA/D. In order to verify the proposed ideal point setting method, we design a set of multi-objective problems(MOPs). The proposed method is compared with the original MOEA/D-TCH and MOEA/D-PBI on MOPs. The experimental results demonstrate that our proposed ideal point setting method performs well in terms of both diversity and convergence.

关键词： multi-objective evolutionary algorithm Ideal Point Setting

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A new optimal dispatch method for the day-ahead electricity market using a multiobjective evolutionary approach

A new optimal dispatch method for the day-ahead electricity ...

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39th North American Power Symposium

作者： Li, Dapeng Pahwa, Anil Das, Sanjoy Rodrigo, Deepal Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Midwest ISO Inc Carmel IN 46032 USA

ISBN: (纸本)9781424417254

In this paper, a novel approach to optimally operate a day-ahead electricity market with considerations of market power is proposed. First, this approach formulates the 1-hour based optimal dispatch problem as a nonlinear constrained multi-objective optimization problem which simultaneously minimizes fuel cost, emissions and the modified Herfindahl-Hirschman index. Then the Non-dominated Sorting Genetic algorithm II (NSGA-II) is used to solve this optimization problem. Simulation results illustrate that this algorithm is capable of finding the Pareto-optimal front in a single run. The best operation schedule for the whole 24 hours can be chosen by taking the ramp rate constraints of generators into account. This approach can provide the market operator with much operating feasibility and thus is more realistic and feasible.

关键词： economic dispatch market power day-ahead electricity market multi-objective evolutionary algorithm

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A Preference-based multi-objective evolutionary Strategy for Ab Initio Prediction of Proteins 5

A Preference-based Multi-objective Evolutionary Strategy for...

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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Zhenyu Song Yajiao Tang Xingqian Chen Shuangbao Song Shuangyu Song Shangce Gao Faculty of Engineering University of Toyama

ISBN: (纸本)9781538619797;9781538619780

Predicting the three-dimensional structure of a protein from its amino acid sequence is an important issue in the field of computational biology and bioinformatics. It remains as an unsolved problem and attract enormous researchers' interests. Different from most conventional methods, we model the protein structure prediction(PSP) problem as a multiobjective optimization problem. A three-objective energy function based on three physical terms is designed to evaluate a protein conformation. A multi-objective evolutionary strategy algorithm coupled with preference information is proposed in this study. The preference information is used in the survival criteria, focusing on the exploration of search process. The experimental results based on five proteins in PDB library demonstrate the effectiveness of proposed method. The analysis of Pareto fronts indicates that the preference information can make solutions diverse in genotypic space. Thus, the proposed method gives a new perspective for solving PSP problems.

关键词： Protein structure prediction multi-objective optimization multi-objective evolutionary algorithm preference information utility function

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A multi-objective evolutionary Approach to Optimize the Morphology of a Six Articulated-Wheeled Robot 4

A Multi-Objective Evolutionary Approach to Optimize the Morp...

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4th IEEE International Conference on Artificial Intelligence with applications in Engineering and Technology (ICAIET)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479979103

This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm hybrid mobile robot optimized hybrid mobile robot

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A multi-objective Design of In-Building Distributed Antenna System Using evolutionary algorithms 1

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39th SGAI International Conference on Techniques and Applications of Artificial Intelligence (AI)

作者： AlShanqiti, Khawla Poon, Kin Shakya, Siddhartha Sleptchenko, Andrei Ouali, Anis Khalifa Univ Emirates ICT Innovat Ctr EBTIC Abu Dhabi U Arab Emirates Khalifa Univ Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

ISBN: (数字)9783030348854

ISBN: (纸本)9783030348854;9783030348847

The increasing data traffic inside buildings requires maintaining good cellular network coverage for indoor mobile users. Passive In-building Distributed Antenna System (IB-DAS) is one of the most efficient methods to provide an indoor solution that meets the signal strength requirements. It is a network of spatially distributed antennas in a building connected to telephone rooms which are then connected to a Base Transmission Station (BTS). These connections are established through passive coaxial cables and splitters. The design of IB-DAS is considered to be challenging due to the power-sharing property resulting in two contradicting objectives: minimizing the power usage at the BTS (long-term cost) and minimizing the design components cost (short-term cost). Different attempts have been made in the literature to solve this problem. Some of them are either lacking the consideration of all necessary aspects or facing scalability issues. Additionally, most of these attempts translate the IB-DAS design into a mono-objective problem, which leads to a challenging task of determining a correct combined objective function with justified weighting factors associated with each objective. Moreover, these approaches do not produce multiple design choices which may not be satisfactory in practical scenarios. In this paper, we propose a multi-objective algorithm for designing IB-DAS. The experimental results show the success of this algorithm to achieve our industrial partner's requirement of providing different design options that cannot be achieved using mono-objective approaches.

关键词： Distributed Antenna System multi-objective evolutionary algorithm Non-dominated Sorting Genetic algorithm Crowding distance Normalization

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